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1. 发明申请

US20100318482A1 Kernels for Identifying Patterns in Datasets Containing Noise or Transformation Invariances 有权
标题翻译：用于识别包含噪声或转换入数的数据集中的模式的内核
公开(公告)号：US20100318482A1
公开(公告)日：2010-12-16
申请号：US12868658
申请日：2010-08-25
申请人： Peter L. Bartlett , André Elisseeff , Bernhard Schoelkopf , Olivier Chapelle
发明人： Peter L. Bartlett , André Elisseeff , Bernhard Schoelkopf , Olivier Chapelle
IPC分类号： G06F15/18
CPC分类号： G06K9/623 , G06F19/24 , G06K9/6215 , G06K9/6248 , G06K9/6269 , G06N99/005 , G06Q20/042
摘要： Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets include an invariance transformation or noise, tangent vectors are defined to identify relationships between the invariance or noise and the training data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel, which may be based on a kernel PCA map.
摘要翻译：使用学习机器（如支持向量机）分析数据集，以使用根据要分析的数据的性质选择的内核来识别数据集中的模式。在数据集包括不变性变换或噪声的情况下，定义向量以识别不变性或噪声与训练数据点之间的关系。使用正切向量形成协方差矩阵，然后用于生成内核，其可以基于内核PCA映射。

2. 发明授权

US07788193B2 Kernels and methods for selecting kernels for use in learning machines 失效
标题翻译：内核和选择用于学习机器的内核的方法
公开(公告)号：US07788193B2
公开(公告)日：2010-08-31
申请号：US11929354
申请日：2007-10-30
申请人： Peter L. Bartlett , André Elisseeff , Bernhard Schoelkopf , Olivier Chapelle
发明人： Peter L. Bartlett , André Elisseeff , Bernhard Schoelkopf , Olivier Chapelle
IPC分类号： G06F15/18 , G06F17/00 , G06N5/00
CPC分类号： G06K9/623 , G06F19/24 , G06K9/6215 , G06K9/6248 , G06K9/6269 , G06N99/005 , G06Q20/042
摘要： Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets possesses structural characteristics, locational kernels can be utilized to provide measures of similarity among data points within the dataset. The locational kernels are then combined to generate a decision function, or kernel, that can be used to analyze the dataset. Where an invariance transformation or noise is present, tangent vectors are defined to identify relationships between the invariance or noise and the data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel.
摘要翻译：使用学习机器（如支持向量机）分析数据集，以使用根据要分析的数据的性质选择的内核来识别数据集中的模式。在数据集具有结构特征的情况下，可以利用位置内核提供数据集中的数据点之间的相似度度量。然后组合位置内核以生成可用于分析数据集的决策函数或内核。在存在不变变换或噪声的情况下，定义向量以识别不变性或噪声与数据点之间的关系。使用切向矢量形成协方差矩阵，然后用于生成内核。

3. 发明申请

US20080301070A1 KERNELS AND METHODS FOR SELECTING KERNELS FOR USE IN LEARNING MACHINES 失效
标题翻译：选择用于学习机器的KERNELS的知识和方法
公开(公告)号：US20080301070A1
公开(公告)日：2008-12-04
申请号：US11929354
申请日：2007-10-30
申请人： Peter L. Bartlett , Andre Elisseeff , Bernhard Schoelkopf , Olivier Chapelle
发明人： Peter L. Bartlett , Andre Elisseeff , Bernhard Schoelkopf , Olivier Chapelle
IPC分类号： G06F15/18
CPC分类号： G06K9/623 , G06F19/24 , G06K9/6215 , G06K9/6248 , G06K9/6269 , G06N99/005 , G06Q20/042
摘要： Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets possesses structural characteristics, locational kernels can be utilized to provide measures of similarity among data points within the dataset. The locational kernels are then combined to generate a decision function, or kernel, that can be used to analyze the dataset. Where an invariance transformation or noise is present, tangent vectors are defined to identify relationships between the invariance or noise and the data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel.
摘要翻译：使用学习机器（如支持向量机）分析数据集，以使用根据要分析的数据的性质选择的内核来识别数据集中的模式。在数据集具有结构特征的情况下，可以利用位置内核提供数据集中的数据点之间的相似度度量。然后组合位置内核以生成可用于分析数据集的决策函数或内核。在存在不变变换或噪声的情况下，定义向量以识别不变性或噪声与数据点之间的关系。使用切向矢量形成协方差矩阵，然后用于生成内核。

4. 发明授权

US07353215B2 Kernels and methods for selecting kernels for use in learning machines 有权
标题翻译：内核和选择用于学习机器的内核的方法
公开(公告)号：US07353215B2
公开(公告)日：2008-04-01
申请号：US10477078
申请日：2002-05-07
申请人： Peter L. Bartlett , André Elisseeff , Bernhard Schoelkopf
发明人： Peter L. Bartlett , André Elisseeff , Bernhard Schoelkopf
IPC分类号： G06F17/00 , G06N5/02
CPC分类号： G06K9/623 , G06F19/24 , G06K9/6215 , G06K9/6248 , G06K9/6269 , G06N99/005 , G06Q20/042
摘要： Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets possesses structural characteristics, locational kernels can be utilized to provide measures of similarity among data points within the dataset. The locational kernels are then combined to generate a decision function, or kernel, that can be used to analyze the dataset. Where invariance transformations or noise is present, tangent vectors are defined to identify relationships between the invariance or noise and the data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel for recognizing patterns in the dataset.
摘要翻译：使用学习机器（如支持向量机）分析数据集，以使用根据要分析的数据的性质选择的内核来识别数据集中的模式。在数据集具有结构特征的情况下，可以利用位置内核提供数据集中的数据点之间的相似度度量。然后组合位置内核以生成可用于分析数据集的决策函数或内核。在存在不变性变换或噪声的情况下，定义向量以识别不变性或噪声与数据点之间的关系。使用切向量形成协方差矩阵，然后用于生成用于识别数据集中的模式的内核。

5. 发明授权

US07318051B2 Methods for feature selection in a learning machine 有权
标题翻译：学习机器中特征选择的方法
公开(公告)号：US07318051B2
公开(公告)日：2008-01-08
申请号：US10478192
申请日：2002-05-20
申请人： Jason Aaron Edward Weston , André Elisseeff , Bernhard Schoelkopf , Fernando Pérez-Cruz
发明人： Jason Aaron Edward Weston , André Elisseeff , Bernhard Schoelkopf , Fernando Pérez-Cruz
IPC分类号： G06F15/18 , G06E1/00 , G06E3/00
CPC分类号： G06K9/6215 , G06K9/623 , G06K9/6231 , G06K9/6256 , G06K9/6262 , G06K9/6269 , G06N99/005 , G09B5/00 , G09B7/00
摘要： In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (lo-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score and transductive feature selection. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection. (FIG. 3, 300, 301, 302, 304, 306, 308, 309, 310, 311, 312, 314)
摘要翻译：在训练学习机之前的预处理步骤中，预处理包括使用从递归特征消除（RFE）中选出的特征选择方法来减少要处理的特征量的数量，使非零参数的数量最小化的系统（最小化），评估成本函数以识别与由学习集施加的约束兼容的特征的子集，不平衡相关得分和转换特征选择。然后，特征选择之后剩余的特征用于训练学习机，用于模式分类，回归，聚类和/或新颖性检测。（图3），300,301,302,304,306,308,309,310,311,312,314,314，

6. 发明授权

US08209269B2 Kernels for identifying patterns in datasets containing noise or transformation invariances 有权
标题翻译：用于识别包含噪声或转换不变性的数据集中的模式的内核
公开(公告)号：US08209269B2
公开(公告)日：2012-06-26
申请号：US12868658
申请日：2010-08-25
申请人： Bernhard Schoelkopf , Olivier Chapelle
发明人： Bernhard Schoelkopf , Olivier Chapelle
IPC分类号： G06F15/18 , G06F17/00 , G06N5/00
CPC分类号： G06K9/623 , G06F19/24 , G06K9/6215 , G06K9/6248 , G06K9/6269 , G06N99/005 , G06Q20/042
摘要： Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets include an invariance transformation or noise, tangent vectors are defined to identify relationships between the invariance or noise and the training data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel, which may be based on a kernel PCA map.
摘要翻译：使用学习机器（如支持向量机）分析数据集，以使用根据要分析的数据的性质选择的内核来识别数据集中的模式。在数据集包括不变性变换或噪声的情况下，定义向量以识别不变性或噪声与训练数据点之间的关系。使用正切向量形成协方差矩阵，然后用于生成内核，其可以基于内核PCA映射。

7. 发明授权

US07444015B2 Interactive images 有权
标题翻译：交互式图像
公开(公告)号：US07444015B2
公开(公告)日：2008-10-28
申请号：US11020926
申请日：2004-12-22
申请人： Bernhard Schoelkopf , Kentaro Toyama , Matthew Uyttendaele
发明人： Bernhard Schoelkopf , Kentaro Toyama , Matthew Uyttendaele
IPC分类号： G06K9/00 , G06F17/00 , G06T1/00
CPC分类号： G06T11/001
摘要： A system and process for creating an interactive digital image, which allows a viewer to interact with a displayed image so as to change it with regard to a desired effect, such as exposure, focus or color, among others. An interactive image includes representative images which depict a scene with some image parameter varying between them. The interactive image also includes an index image, whose pixels each identify the representative image that exhibits the desired effect related to the varied image parameter at a corresponding pixel location. For example, a pixel of the index image might identify the representative image having a correspondingly-located pixel that depicts a portion of the scene at the sharpest focus. One primary form of interaction involves selecting a pixel of a displayed image whereupon the representative image identified in the index image at a corresponding pixel location is displayed in lieu of the currently displayed image.
摘要翻译：一种用于创建交互式数字图像的系统和过程，其允许观看者与显示的图像交互，以便根据期望的效果（例如曝光，聚焦或颜色等）来改变它。交互式图像包括描绘具有在它们之间变化的一些图像参数的场景的代表性图像。交互式图像还包括索引图像，其像素每个标识在相应像素位置呈现与变化的图像参数相关的期望效果的代表图像。例如，索引图像的像素可以识别具有对应位置的像素的代表图像，其描绘最尖锐焦点的场景的一部分。一种主要的交互形式涉及选择显示图像的像素，由此显示在相应像素位置处的索引图像中标识的代表图像来代替当前显示的图像。

8. 发明授权

US07120293B2 Interactive images 有权
公开(公告)号：US07120293B2
公开(公告)日：2006-10-10
申请号：US10012001
申请日：2001-11-30
申请人： Bernhard Schoelkopf , Kentaro Toyama , Matthew T. Uyttendaele
发明人： Bernhard Schoelkopf , Kentaro Toyama , Matthew T. Uyttendaele
IPC分类号： G06K9/00 , G06F17/00
CPC分类号： G06T11/001
摘要： A system and process for creating an interactive digital image, which allows a viewer to interact with a displayed image so as to change it with regard to a desired effect, such as exposure, focus or color, among others. An interactive image includes representative images which depict a scene with some image parameter varying between them. The interactive image also includes an index image, whose pixels each identify the representative image that exhibits the desired effect related to the varied image parameter at a corresponding pixel location. For example, a pixel of the index image might identify the representative image having a correspondingly-located pixel that depicts a portion of the scene at the sharpest focus. One primary form of interaction involves selecting a pixel of a displayed image whereupon the representative image identified in the index image at a corresponding pixel location is displayed in lieu of the currently displayed image.

9. 发明授权

US07099504B2 Pattern detection methods and systems and face detection methods and systems 失效
标题翻译：模式检测方法和系统以及面部检测方法和系统
公开(公告)号：US07099504B2
公开(公告)日：2006-08-29
申请号：US10850883
申请日：2004-05-21
申请人： Andrew Blake , Sami Romdhani , Bernhard Schoelkopf , Philip H. S. Torr
发明人： Andrew Blake , Sami Romdhani , Bernhard Schoelkopf , Philip H. S. Torr
IPC分类号： G06K9/62
CPC分类号： G06K9/6269 , G06K9/00228
摘要： Systems and methods for object or pattern detection that use a nonlinear support vector (SV) machine are described. In the illustrated and described embodiment, objects or patterns comprising faces are detected. The decision surface is approximated in terms of a reduced set of expansion vectors. In order to determine the presence of a face, the kernelized inner product of the expansion vectors with the input pattern are sequentially evaluated and summed, such that if at any point the pattern can be rejected as not comprising a face, no more expansion vectors are used. The sequential application of the expansion vectors produces a substantial saving in computational time.
摘要翻译：描述了使用非线性支持向量（SV）机器的对象或模式检测的系统和方法。在所示出的和描述的实施例中，检测包括面部的对象或图案。决策表面根据缩减的扩展向量集来近似。为了确定面部的存在，扩展向量与输入图案的核心化内积被顺序地估计和相加，使得如果在任何点上可以将图案拒绝为不包括面部，则不再有扩展向量用过的。扩展向量的顺序应用可大大节省计算时间。

10. 发明申请

US20050157933A1 Pattern detection using reduced set vectors 有权
标题翻译：使用缩减集矢量的模式检测
公开(公告)号：US20050157933A1
公开(公告)日：2005-07-21
申请号：US11069229
申请日：2005-02-28
申请人： Andrew Blake , Sami Romdhani , Bernhard Schoelkopf , Philip Torr
发明人： Andrew Blake , Sami Romdhani , Bernhard Schoelkopf , Philip Torr
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/6269 , G06K9/00228
摘要： Systems and methods for object or pattern detection that use a nonlinear support vector (SV) machine are described. In the illustrated and described embodiment, objects or patterns comprising faces are detected. The decision surface is approximated in terms of a reduced set of expansion vectors. In order to determine the presence of a face, the kernelized inner product of the expansion vectors with the input pattern are sequentially evaluated and summed, such that if at any point the pattern can be rejected as not comprising a face, no more expansion vectors are used. The sequential application of the expansion vectors produces a substantial saving in computational time.
摘要翻译：描述了使用非线性支持向量（SV）机器的对象或模式检测的系统和方法。在所示出的和描述的实施例中，检测包括面部的对象或图案。决策表面根据缩减的扩展向量集来近似。为了确定面部的存在，扩展向量与输入图案的核心化内积被顺序地估计和相加，使得如果在任何点上可以将图案拒绝为不包括面部，则不再有扩展向量用过的。扩展向量的顺序应用可大大节省计算时间。

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